AB1377 FULLY AUTOMATIC ASSESSMENT OF NAIL FOLD CAPILLAROSCOPY SOFTWARE – PILOT STUDY
Authors: O. Brzezińska, K. Rychlicki-Kicior, J. Makowska
Journal: Annals of the Rheumatic Diseases (2022)

Background The nail fold capillaroscopy is one of the basic techniques used in diagnosis and monitoring the course of connective tissue diseases, primarily a systemic sclerosis. However, the assessment of the capillary image is time-consuming and subjective, this makes it difficult for a detailed comparison of studies assessed by various physicians. Objectives The aim of this study was to validate an automated software for classification the nail fold capillaroscopy as normal or pathological and counting the numerous of vessels on a millimetre. Methods 100 correct and 100 pathological images were selected from the capillaroscopic image database. The original examination was performed with Dinolite MEDL4N Pro. The classic, manual evaluation of the capillaroscopy was performed twice. The imagines was classified as correct or pathological and mean number of capillaries per millimetre was calculated. The photos were then exported to the training program created for the study, the region of interest (ROI) and individual capillaries were marked. The calculation of the number of vessels was made at a magnification of about 50x (ensuring the maximum quality of the image obtained during a classic examination). The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen, 10-fold cross-validation with validation and test set was performed, using Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For calculation of 1mm capillaries, additional detection mechanism was designed, in order to automatically detect the scale of the image and transform it into proper pixel dimensions. Results The results obtained under neural network training have been referred to the results obtained as part of a manual photo assessment. For the image classification correct vs pathological test sensitivity 89.0% and specificity 86.9% was obtained. For validation (20% of images have been drawn as a validating group), 89.0% and 89.4% respectively were noticed. For the average number of capillaries in 1mm, an average of three regions have been estimated (the first region with 1mm from the left, the second with 1mm from the right and the third with 1mm in the middle). The estimated number of capillars in each region has been compared with ground truth data. The RMSE (Root Mean Squared Error) for the test set was 2.81 and the accuracy was 76.2%. Conclusion The use of fully automatic Nail Fold Capillaroscopy Software can be a fast and easy method of the correct and changed capillaroscopy pattern classification, the possibility of counting gives a repetitive result regardless of the physician evaluating the capillaroscopy pattern. In the further course of work on the software, it will be possible to distinguish megacapillary, branched vessels, avascularisation and other pathological features, which will allow full automatic image evaluation and easy follow-up of patients. Disclosure of Interests None declared